In this study, we implemented a high-throughput transcriptome sequencing platform integrated with machine learning-enhanced predictive analytics to systematically screen 188 medicinal plant extracts for anti-neoplastic bioactivity. Leveraging multi-layered omics interrogation—including differential gene expression profiling, weighted gene co-expression network analysis (WGCNA), and Signaling pathway enrichment—we delineated phytochemical candidates with dual targeting capacity against metastatic drivers and inflammatory mediators. Subsequent Phosphoproteomic profiling confirmed the therapeutic strategies of lead compounds. This methodology establishes a transformative paradigm for natural product drug discovery, combining next-generation sequencing with artificial intelligence-driven bioactivity prediction.